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检索条件"任意字段=3rd IEEE International Conference on Distributed Computing in Sensor Systems"

共 5112 条记录，以下是31-40 订阅

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8th international conference on Autonomic computing, ICAC 2011 Co-located Workshops - Proceedings of the 3rd Workshop on Biologically Inspired Algorithms for distributed systems, BADS 2011

8th International Conference on Autonomic Computing, ICAC 20...

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3rd Workshop on Bio-Inspired and Self-Algorithms for distributed systems, Co-located with the 8th ieee/ACM international conference on Autonomic computing, ICAC 2011

ISBN: (纸本)9781450307338

The proceedings contain 7 papers. The topics discussed include: memetic algorithm for web service selection;model-driven performance engineering for wireless sensor networks with feature modeling and event calculus;description and composition of bio-inspired design patterns: the gradient case;methods for self-organizing distributed software;protein structure prediction using particle swarm optimization and a distributed parallel approach;discrete optimization problem solving with three variants of hybrid binary particle swarm optimization;and self-organized invasive parallel optimization.

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Collaborative Current Sharing Control Strategy for distributed DC Heterogeneous systems 3rd

Collaborative Current Sharing Control Strategy for Distribut...

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3rd international conference on Information Control, Electrical Engineering, and Rail Transit, ICEERT 2023

作者： Li, Zhenhui Liu, Xiaodong Zhu, Gui Zhang, Qianjin Liu, Sucheng Fang, Wei School of Electrical and Information Engineering Anhui University of Technology Maanshan243032 China

ISBN: (纸本)9789819746194

As renewable energy sources in the grid increase, the utilization of distributed power sources has become a research focus, with the parallel connection of multiple small power sources to provide high-power output becoming a research hotspot. When two systems with different current sharing modes are connected in parallel, current sharing cannot be realized because information cannot be exchanged through communication links. Therefore, the Hall sensor is used in this study to collect the output current of the original system. Since the current sharing control strategy already exists in the original system, the average sampling current is taken as the output current when the original system reaches the current equalization state. Based on the historical data and the diffusion algorithm, the initial current value and the average normalized value of the power module in the new system are obtained. The output current is adjusted by module proportional operation, and the sample mean is used as the reference information of output current between different systems to realize output current adjustment. Finally, a simulation model is built in Matlab/Simulink, and the effectiveness of the proposed control strategy is verified by experiments, and the collaborative current sharing control between different systems is realized. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric power transmission

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A Hybrid PSO and K-Means Clustering Approach for Enhanced sensor Localization in WSNs 3

A Hybrid PSO and K-Means Clustering Approach for Enhanced Se...

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3rd international conference on Intelligent systems, Advanced computing, and Communication, ISACC 2025

作者： Singh, Ningombam Hemarjit Manna, Prithwish Majumder, Sudipta Bose, Rajesh Dibrugarh University Dept. of CSE Dibrugarh India JIS University Dept. of CSE West Bengal India

ISBN: (纸本)9798331523893

In Wireless sensor Networks (WSNs), correct sensor location is crucial for optimal data collection and transmission. This work introduces an improved methodology for sensor placement that integrates Particle Swarm Optimisation (PSO) with K-Means clustering to boost the efficacy of RSSI-based localisation. To measure the distance between sensors, researchers commonly employ the Received Signal Strength Indicator (RSSI);nevertheless, errors may be introduced by outside variables including interference and signal attenuation. To tackle these problems, we present a hybrid approach whereby Particle Swarm Optimisation (PSO) is employed for the global optimisation of sensor placements, while K-Means clustering enhances the sensor aggregation based on proximity and Received Signal Strength Indicator (RSSI) signal intensity. The suggested technique reduces localisation errors by optimising sensor placement and classifying nodes into suitable clusters. Experimental findings indicate that the hybrid PSO-K-Means method markedly improves positioning accuracy relative to conventional localisation strategies. The results indicate that combining PSO with K-Means clustering is an efficient approach for enhancing sensor localisation for the considered performance criteria. © 2025 ieee.

关键词： K-Means Clustering Particle Swarm Optimization (PSO) RSSI-Based Localization Wireless sensor Networks (WSNs)

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A Hybrid Optimization Strategy for k-Coverage Maximization in Wireless sensor and IoT Networks 3

A Hybrid Optimization Strategy for k-Coverage Maximization i...

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3rd international conference on Intelligent systems, Advanced computing, and Communication, ISACC 2025

作者： Manna, Prithwish Majumder, Sudipta Singh, N. Hemarjit Dibrugarh University Dept. of CSE DUIET Assam Dibrugarh India Dibrugarh University Dept. of ECE DUIET Assam Dibrugarh India

ISBN: (纸本)9798331523893

In this paper, we propose a combination of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to create a hybrid optimization technique which meets k-coverage criteria in Wireless sensor Network (WSN) and Internet of Things (IoT). The goal of this approach is to increase the coverage area alongside minimizing the deployment cost of the sensors. Another aim is to reduce the power consumption. This technique is helpful to determine the optimal position of the sensors. It helps to ensure that each target point in the network is getting the coverage of at least k sensors. In this way we can enhance the reliability and robustness of the network. From experimental results, we can conclude that with optimal sensor utilization and minimized redundancy, our proposed approach achieves 97.14% coverage ratio with k-coverage equals 3. According to performance metrics, it is clear that there is consistent energy efficiency through the cycles. The approach converges in less than 3 second. So, this approach is applicable for real time applications. It is a hybrid metaheuristic approach which is very efficient in managing various challenges associated with sensor deployment. From this approach we can get solutions of coverage enhancement, energy efficiency and improved sensor utilization. © 2025 ieee.

关键词： Energy Efficiency Genetic Algorithm K-Coverage Particle Swarm Optimization sensor Deployment Wireless sensor Network

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Application and Evaluation of Real-Time Analysis Algorithms in Dynamic Data Processing systems

Application and Evaluation of Real-Time Analysis Algorithms ...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Si Chen College of Digital Business Liaoning Vocational University of Technology Jinzhou Liaoning China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Dynamic data processing systems are key technologies in modern data-driven applications, widely used in fields such as the Internet of Things, finance and healthcare. This study explores the application and evaluation of real-time analytical algorithms for dynamic data processing. An efficient algorithmic framework combining adaptive windowing, incremental learning and distributed computing is proposed to achieve low-latency and high-accuracy decision-making capabilities. Through experimental validation in a real data streaming environment, the results show that the proposed algorithm significantly outperforms traditional algorithms with about 35% reduction in latency and 20% improvement in prediction accuracy. The research results in this paper show that the real-time analysis algorithm has a promising application in dealing with dynamic heterogeneous data streams. Meanwhile, the article also discusses the limitations and future optimisation directions of the method, which provides a reference for further research in related fields.

关键词： Performance evaluation Incremental learning Accuracy Heuristic algorithms Data processing Prediction algorithms Real-time systems Low latency communication distributed computing Streams

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Smart Lighting systems for Energy Efficiency Using IoT and sensor Technologies 3

Smart Lighting Systems for Energy Efficiency Using IoT and S...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Swain, Biswaranjan Khurana, Shivam Krishna, Gautham Radhika, S. Alam, Intekhab Faiz, Ayaan Centre for Internet of Things Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Department of Civil Engineering Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798331542375

Smart Lighting systems for Energy Efficiency via Internet of Things (IoT) and sensor Technologies A novel method of attaining energy efficiency is combining IoT with sensor technology. The emergence of the IOT given go up to intelligent lighting systems that can leverage sensors, data analytics, and connectivity means for energy optimization. Their purpose is to adjust lighting levels automatically regarding occupancy, daylight availability, and time of day to save significant amounts of power. The sensors are wireless;hence, they are battery-powered IoT devices that measure the ambient light level at the location where they were installed. It also measures movement in their range and temperature readings for an office or conference area, enabling smart lighting systems to adapt to local conditions while minimizing energy wastage. This mechanism of collecting real-time data and analyzing the same gives us insights on patterns or preferences in lighting usage thus can be customized for different type's timings. This increases the effective use of artificial lighting and ensures it takes advantage of natural light availability. In addition to this, these systems can also integrate with other smart building technologies like heating and cooling systems for a well-rounded approach to energy management. The lighting control from mobile devices enhances building owners' and managers' convenience and to manage their property features remotely. © 2025 ieee.

关键词： Battery management systems

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Real-Time Monitoring of Structural Health with Wireless sensor Networks in IoT Infrastructure 3

Real-Time Monitoring of Structural Health with Wireless Sens...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Bhushan, Bharat Pushparajesh, V. Bethanney Janney, J. Sahoo, Girija Shankar Aggarwal, Prateek Das, Dibyasundar Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Centre for Cyber Security Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

Wireless sensor Networks for structural health monitoring of infrastructure are now becoming an IoT. It provides the ability to collect and analyze survivability data in real-time to enable continuous structural health monitoring for buildings, bridges, and other critical infrastructure. The purpose of this abstract is to introduce Real-time Monitoring with Wireless sensor Networks in IoT Infrastructure. Wireless sensor Networks are used in structural health monitoring. They are typically composed of many distributed, cheap sensors embedded in the structure that communicate wirelessly to a central monitoring station. Those sensors, including temperature, strain and vibration, are critical in detecting potential structural problems. This data gets transmitted to a cloud for processing, which provides indications of an unusual characteristic or predictive failure. Compared to the traditional methods, there are several advantages of applying WSN for real-time structural health monitoring. Lead leads provide real-time updates on the condition of a bridge that allows for continuous monitoring, with advanced sensors pinpointed through its structure. It will assist in keeping the infrastructure safe and affordable by guaranteeing rapid time-to-resolution. In addition, WSNs have low implementation costs and do not interfere too much with the structure of large structures. © 2025 ieee.

关键词： Wireless sensor networks

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sensor Networks for Real-Time Air Quality Monitoring in Smart Cities 3

Sensor Networks for Real-Time Air Quality Monitoring in Smar...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Das, Neeraj Katyal, Ashu Dutta, Pratik Aggarwal, Prateek Pavan, P.S. Vincent, Kalist Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

This requires ubiquitous sensor networks to monitor and instantaneously predict dangerous gas concentrations at ground level deployed within smart city environments. A sensor network is a collection of many distributed sensors that can be used together to gather data in real-time and from standard logs across an environment, generally allowing users to see, hear, or feel whether emissions are entering their air as they happen. Cities attaching these sensor networks can provide real-time air quality data for municipalities and citizens taking control of our health. It can analyze pollution hotspots, detect trends and patterns over time, and act as an early warning system for dangers. Such work creates a framework for interventions to be undertaken quickly and efficiently to reduce air pollution as soon as possible and work towards protecting residents' public health. sensor networks can also provide historical data, which is necessary for tracking long-term trends and assessing how effective pollution control measures have been. Furthermore, this information can validate city-specific policies and exercise recommendations for reducing air pollution in smart cities. It will give decision-makers and citizens the data they must act on while making well-informed decisions for cleaner air in their cities. © 2025 ieee.

关键词： Air quality

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IOT and sensor-Based Flood Monitoring and Warning systems for Disaster Management 3

IOT and Sensor-Based Flood Monitoring and Warning Systems fo...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Chitra, P. Acharjya, Kalyan Ojha, Varun Das, Priyadarshi Faiz, Ayaan Rao, Srikrishna Baskar Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Civil Engineering Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

The Internet of Things, which has sensors for monitoring floods and generating alerts, reflects the most contemporary use-case of these solutions in disaster management. Using linked devices and sensors, these systems gather real-time information concerning water levels, weather aspects, and other environmental factors associated with flooding. This data can now produce precise flood forecasts and alerts, giving communities the information they need for large weather events with a better idea of when disasters may strike. Advantages of IOT and sensor-based flood monitoring systems: sensor cadmium selenium photocells, sharp water & infrared sensors, which include water level sensors, rain gauges, and temperature sensors in places exposed to floods. They do this by involving sensors installed throughout the city that monitor water levels or rainfall and can relay fluctuations to a central box. The data is then processed by sophisticated algorithms, resulting in flood forecasts and alerts that can be accessed via web-based platforms or mobile apps. One significant benefit of these systems is real-time flood data with accurate results that help communities evacuate and other disaster mitigation actions. These systems can also be linked to established emergency response systems that serve as an information source for graduate disaster management. These sensors and IOT-based flood monitoring systems also make for great innovative Disaster Management tools that will increase awareness, leading to better preparedness and response time between the occurrences of a natural disaster like floods with communities. © 2025 ieee.

关键词： Information management

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Smart Transportation systems: The Role of IoT and sensor-Based Solutions 3

Smart Transportation Systems: The Role of IoT and Sensor-Bas...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Agarwal, Ankita Seth, Kanika Sahoo, Shreeya Swagatika Parmar, Yuvraj Dasarathy, A.K. Siva Sangari, A. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Centre for Cyber Security Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Department of Civil Engineering Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

Smart transportation systems are sophisticated technology solutions that deploy the Internet of Things (IoT) and sensor-based data platforms to transform one's existing erratic transportation network into an efficient system. The Internet of Things, which combines all devices and systems connected to the Internet, is changing our management and operation of transportation systems. Real-time data from sensors installed on vehicles, infrastructure, and other transport assets is one of the biggest benefits IoT can offer to transportation. This data will reduce traffic congestion, improve road safety, and help buses move smoothly in terms of traffic flow. A good way of illustrating this is through sensors equipped with traffic lights that communicate to the cars, where they can adjust between red and green light times while in action, thus causing fewer accidents on the road. loT likewise can lessen energy consumption and emissions, driving by way of the sustainability lens on transportation systems. Smart traffic management systems can adjust routes for vehicles, saving fuel and emissions. Cars and even electric vehicles can be configured using the same platform through this system will deliver higher efficiency for managing charging stations. Additionally, IoT and sensor integration are important for the growth of autonomous vehicles. These vehicles need immediate input from cameras, radars, and LiDARs to move around safely. IoT will allow these vehicles to communicate, and the transportation infrastructure will lead to a safer means of transport. © 2025 ieee.

关键词： Traffic congestion

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